ANAT: a tool for constructing and analyzing functional protein networks

phuEGO: A Network-Based Method to Reconstruct Active Signaling Pathways From Phosphoproteomics Datasets

Signaling networks are critical for virtually all cell functions. Our current knowledge of cell signaling has been summarized in signaling pathway databases, which, while useful, are highly biased toward well-studied processes, and do not capture context specific network wiring or pathway cross-talk. Mass spectrometry-based phosphoproteomics data can provide a more unbiased view of active cell signaling processes in a given context, however, it suffers from low signal-to-noise ratio and poor reproducibility across experiments. While progress in methods to extract active signaling signatures from such data has been made, there are still limitations with respect to balancing bias and interpretability. Here we present phuEGO, which combines up-to-three-layer network propagation with ego network decomposition to provide small networks comprising active functional signaling modules. PhuEGO boosts the signal-to-noise ratio from global phosphoproteomics datasets, enriches the resulting networks for functional phosphosites and allows the improved comparison and integration across datasets. We applied phuEGO to five phosphoproteomics data sets from cell lines collected upon infection with SARS CoV2. PhuEGO was better able to identify common active functions across datasets and to point to a subnetwork enriched for known COVID-19 targets. Overall, phuEGO provides a flexible tool to the community for the improved functional interpretation of global phosphoproteomics datasets.

Exosomal telomerase transcripts reprogram the microRNA transcriptome profile of fibroblasts and partially contribute to CAF formation

It is now well accepted that cancer cells change their microenvironment from normal to tumor-supportive state to provide sustained tumor growth, metastasis and drug resistance. These processes are partially carried out by exosomes, nano-sized vesicles secreted from cells, shuttled from donor to recipient cells containing a cargo of nucleic acids, proteins and lipids. By transferring biologically active molecules, cancer-derived exosomes may transform microenvironmental cells to become tumor supportive. Telomerase activity is regarded as a hallmark of cancer. We have recently shown that the transcript of human telomerase reverse transcriptase (hTERT), is packaged in cancer cells derived- exosomes. Following the engulfment of the hTERT transcript into fibroblasts, it is translated into a fully active enzyme [after assembly with its RNA component (hTERC) subunit]. Telomerase activity in the recipient, otherwise telomerase negative cells, provides them with a survival advantage. Here we show that exosomal telomerase might play a role in modifying normal fibroblasts into cancer associated fibroblasts (CAFs) by upregulating \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{\alpha }$$\end{document}αSMA and Vimentin, two CAF markers. We also show that telomerase activity changes the transcriptome of microRNA in these fibroblasts. By ectopically expressing microRNA 342, one of the top identified microRNAs, we show that it may mediate the proliferative phenotype that these cells acquire upon taking-up exosomal hTERT, providing them with a survival advantage.

Gene architecture directs splicing outcome in separate nuclear spatial regions

How the splicing machinery defines exons or introns as the spliced unit has remained a puzzle for 30 years. Here, we demonstrate that peripheral and central regions of the nucleus harbor genes with two distinct exon-intron GC content architectures that differ in the splicing outcome. Genes with low GC content exons, flanked by long introns with lower GC content, are localized in the periphery, and the exons are defined as the spliced unit. Alternative splicing of these genes results in exon skipping. In contrast, the nuclear center contains genes with a high GC content in the exons and short flanking introns. Most splicing of these genes occurs via intron definition, and aberrant splicing leads to intron retention. We demonstrate that the nuclear periphery and center generate different environments for the regulation of alternative splicing and that two sets of splicing factors form discrete regulatory subnetworks for the two gene architectures. Our study connects 3D genome organization and splicing, thus demonstrating that exon and intron definition modes of splicing occur in different nuclear regions.

Data on dose-dependent cytotoxicity of rotenone and neuroprotection conferred by Yashtimadhu (Glycyrrhiza glabra L.) in an in vitro Parkinson's disease model

The data described in this article presents the toxicity of rotenone and the neuroprotective effect of Yashtimadhu choorna (powder) in an in vitro Parkinson's disease model [1]. Yashtimadhu choorna is prepared from the roots of Glycyrrhiza glabra L., commonly known as licorice/ liquorice. The effects of rotenone and Yashtimadhu was assessed using cellular and molecular assays such as cell cytotoxicity assay, live-dead cell staining assay, cell cycle analysis, and western blotting. Protein-protein interaction was studied using ANAT plug-in in Cytoscape. Rotenone displayed time and dose-dependent toxicity, as evidenced by cell cytotoxicity assay and live-dead cell staining assay. Yashtimadhu showed no toxicity and prevented rotenone-induced toxicity. Rotenone and Yashtimadhu displayed differential control on the cell cycle. The Protein-interaction network showed the proteins interacting with ERK-1/2 and the pathways regulated by these interactions. The pathways regulated were primarily involved in cellular oxidative stress and apoptosis response. The data described here will enable the extent of cellular toxicity as a result of rotenone treatment and the neuroprotection conferred by Yashtimadhu choorna. This will enable understanding and exploring the effect of traditional and complementary medicine and aiding the identification of molecular targets to confer neuroprotection in Parkinson's disease.

ANAT 3.0: a framework for elucidating functional protein subnetworks using graph-theoretic and machine learning approaches

BACKGROUND

ANAT is a Cytoscape plugin for the inference of functional protein-protein interaction networks in yeast and human. It is a flexible graphical tool for scientists to explore and elucidate the protein-protein interaction pathways of a process under study.

RESULTS

Here we present ANAT3.0, which comes with updated PPI network databases of 544,455 (human) and 155,504 (yeast) interactions, and a new machine-learning layer for refined network elucidation. Together they improve network reconstruction to more than twofold increase in the quality of reconstructing known signaling pathways from KEGG.

CONCLUSIONS

ANAT3.0 includes improved network reconstruction algorithms and more comprehensive protein-protein interaction networks than previous versions. ANAT is available for download on the Cytoscape Appstore and at https://www.cs.tau.ac.il/~bnet/ANAT/ .

Introducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs)

Integrating -omics data with biological networks such as protein-protein interaction networks is a popular and useful approach to interpret expression changes of genes in changing conditions, and to identify relevant cellular pathways, active subnetworks or network communities. Yet, most -omics data integration tools are restricted to static networks and therefore cannot easily be used for analyzing time-series data. Determining regulations or exploring the network structure over time requires time-dependent networks which incorporate time as one component in their structure. Here, we present a method to project time-series data on sequential layers of a multilayer network, thus creating a temporal multilayer network (tMLN). We implemented this method as a Cytoscape app we named TimeNexus. TimeNexus allows to easily create, manage and visualize temporal multilayer networks starting from a combination of node and edge tables carrying the information on the temporal network structure. To allow further analysis of the tMLN, TimeNexus creates and passes on regular Cytoscape networks in form of static versions of the tMLN in three different ways: (i) over the entire set of layers, (ii) over two consecutive layers at a time, (iii) or on one single layer at a time. We combined TimeNexus with the Cytoscape apps PathLinker and AnatApp/ANAT to extract active subnetworks from tMLNs. To test the usability of our app, we applied TimeNexus together with PathLinker or ANAT on temporal expression data of the yeast cell cycle and were able to identify active subnetworks relevant for different cell cycle phases. We furthermore used TimeNexus on our own temporal expression data from a mouse pain assay inducing hindpaw inflammation and detected active subnetworks relevant for an inflammatory response to injury, including immune response, cell stress response and regulation of apoptosis. TimeNexus is freely available from the Cytoscape app store at https://apps.cytoscape.org/apps/TimeNexus .

Evolution of fibroblasts in the lung metastatic microenvironment is driven by stage-specific transcriptional plasticity

Mortality from breast cancer is almost exclusively a result of tumor metastasis, and lungs are one of the main metastatic sites. Cancer-associated fibroblasts are prominent players in the microenvironment of breast cancer. However, their role in the metastatic niche is largely unknown. In this study, we profiled the transcriptional co-evolution of lung fibroblasts isolated from transgenic mice at defined stage-specific time points of metastases formation. Employing multiple knowledge-based platforms of data analysis provided powerful insights on functional and temporal regulation of the transcriptome of fibroblasts. We demonstrate that fibroblasts in lung metastases are transcriptionally dynamic and plastic, and reveal stage-specific gene signatures that imply functional tasks, including extracellular matrix remodeling, stress response, and shaping the inflammatory microenvironment. Furthermore, we identified Myc as a central regulator of fibroblast rewiring and found that stromal upregulation of Myc transcriptional networks is associated with disease progression in human breast cancer.

Mitochondrial ribosomal small subunit proteins (MRPS) MRPS6 and MRPS23 show dysregulation in breast cancer affecting tumorigenic cellular processes

Human Mitoribosomal Small Subunit unit (MRPS) family of genes appears to have role in cancer. Gene expression analysis of select MRPS genes (n = 9) in 15 cancer cell lines showed altered expression in cancer cells. Protein levels of MRPS6, MRPS23 showed significant overexpression in breast cancer cells and tissues. Interestingly, their overexpression did not correlate with mitochondrial ribosome translated COX2 protein levels in breast cancer. Subcellular fractionation analysis showed a distinct presence of MRPS23 in the nuclear fraction. GST/MRP6 and GST/MRPS23 pulldown assays identified 32 novel protein-protein interactions (PPIs) and MRPS23-RIPK3 interaction was validated. Co-expression module identification tool (CEMi) analysis of breast cancer gene expression and MRPS6 and MRPS23 interactions revealed hub interactions in gene expression modules having functional roles in cancer-associated cellular processes. Based on PPI network analysis a novel interaction MRPS23-p53 was validated. Knockdown of MRPS6 and MRPS23 decreased proliferation, expression of select mesenchymal markers, oncogenes, and increased expression of tumor suppressor genes. Taken together present study has revealed that MRPS6 and MRPS23 genes have pro-tumorigenic functions in breast cancer.

Automating parameter selection to avoid implausible biological pathway models

A common way to integrate and analyze large amounts of biological "omic" data is through pathway reconstruction: using condition-specific omic data to create a subnetwork of a generic background network that represents some process or cellular state. A challenge in pathway reconstruction is that adjusting pathway reconstruction algorithms' parameters produces pathways with drastically different topological properties and biological interpretations. Due to the exploratory nature of pathway reconstruction, there is no ground truth for direct evaluation, so parameter tuning methods typically used in statistics and machine learning are inapplicable. We developed the pathway parameter advising algorithm to tune pathway reconstruction algorithms to minimize biologically implausible predictions. We leverage background knowledge in pathway databases to select pathways whose high-level structure resembles that of manually curated biological pathways. At the core of this method is a graphlet decomposition metric, which measures topological similarity to curated biological pathways. In order to evaluate pathway parameter advising, we compare its performance in avoiding implausible networks and reconstructing pathways from the NetPath database with other parameter selection methods across four pathway reconstruction algorithms. We also demonstrate how pathway parameter advising can guide reconstruction of an influenza host factor network. Pathway parameter advising is method agnostic; it is applicable to any pathway reconstruction algorithm with tunable parameters.

Expression and affinity purification of recombinant mammalian Mitochondrial Ribosomal Small Subunit (MRPS) proteins and protein-protein interaction analysis indicate putative role in tumorigenic cellular processes

MRPS group of proteins are structural constituents of the small subunit of mitoribosomes involved in translation. Recent studies indicate role in tumorigenic process, however, unlike cytosolic ribosomal proteins, knowledge on the role of MRPS proteins in alternate cellular processes is very limited. Mapping protein-protein interactions (PPIs) onto known cellular processes can be a valuable tool to identify novel protein functions. In this study, to identify PPIs of MRPS proteins, we have constructed thirty-one GST/MRPS fusion clones. GST/MRPS fusion proteins were confirmed by MALDI-TOF analysis. GST pull-downs were performed using eight GST/MRPS proteins (MRPS9, MRPS10, MRPS11, MRPS18B, MRPS31, MRPS33, MRPS38, MRPS39), GST alone as pull-down control, and HEK293 cell lysate as the source for anchor proteins followed by nLC/MS/MS analysis and probable PPIs of eight MRPS proteins were identified. Three PPIs from GST pull-downs and interaction between six MRPS proteins and p53 previously reported in PPI database were validated. The PPI network analysis revealed putative role in cellular processes with implications for tumorigenesis. Gene expression screening of a cancer cell line panel indicated overexpression of MRPS10 and MRPS31 in breast cancer. Co-expression module identification tool analysis of breast cancer gene expression and MRPS10 and MRPS31 PPIs revealed putative role for PPI with ACADSB in fatty acid oxidation process regulated by brain-derived neurotrophic factor (BDNF) signaling pathway.

Reconstructing signaling pathways using regular language constrained paths

MOTIVATION

High-quality curation of the proteins and interactions in signaling pathways is slow and painstaking. As a result, many experimentally detected interactions are not annotated to any pathways. A natural question that arises is whether or not it is possible to automatically leverage existing pathway annotations to identify new interactions for inclusion in a given pathway.

RESULTS

We present RegLinker, an algorithm that achieves this purpose by computing multiple short paths from pathway receptors to transcription factors within a background interaction network. The key idea underlying RegLinker is the use of regular language constraints to control the number of non-pathway interactions that are present in the computed paths. We systematically evaluate RegLinker and five alternative approaches against a comprehensive set of 15 signaling pathways and demonstrate that RegLinker recovers withheld pathway proteins and interactions with the best precision and recall. We used RegLinker to propose new extensions to the pathways. We discuss the literature that supports the inclusion of these proteins in the pathways. These results show the broad potential of automated analysis to attenuate difficulties of traditional manual inquiry.

AVAILABILITY AND IMPLEMENTATION

https://github.com/Murali-group/RegLinker.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Intragenic MicroRNAs Autoregulate Their Host Genes in Both Direct and Indirect Ways-A Cross-Species Analysis

MicroRNAs (miRNAs) function as master switches for post-transcriptional gene expression. Their genes are either located in the extragenic space or within host genes, but these intragenic miRNA::host gene interactions are largely enigmatic. The aim of this study was to investigate the location and co-regulation of all to date available miRNA sequences and their host genes in an unbiased computational approach. The majority of miRNAs were located within intronic regions of protein-coding and non-coding genes. These intragenic miRNAs exhibited both increased target probability as well as higher target prediction scores as compared to a model of randomly permutated genes. This was associated with a higher number of miRNA recognition elements for the hosted miRNAs within their host genes. In addition, strong indirect autoregulation of host genes through modulation of functionally connected gene clusters by intragenic miRNAs was demonstrated. In addition to direct miRNA-to-host gene targeting, intragenic miRNAs also appeared to interact with functionally related genes, thus affecting their host gene function through an indirect autoregulatory mechanism. This strongly argues for the biological relevance of autoregulation not only for the host genes themselves but, more importantly, for the entire gene cluster interacting with the host gene.

A systematic approach to orient the human protein-protein interaction network

The protein-protein interaction (PPI) network of an organism serves as a skeleton for its signaling circuitry, which mediates cellular response to environmental and genetic cues. Understanding this circuitry could improve the prediction of gene function and cellular behavior in response to diverse signals. To realize this potential, one has to comprehensively map PPIs and their directions of signal flow. While the quality and the volume of identified human PPIs improved dramatically over the last decade, the directions of these interactions are still mostly unknown, thus precluding subsequent prediction and modeling efforts. Here we present a systematic approach to orient the human PPI network using drug response and cancer genomic data. We provide a diffusion-based method for the orientation task that significantly outperforms existing methods. The oriented network leads to improved prioritization of cancer driver genes and drug targets compared to the state-of-the-art unoriented network.

Computational Methods for Understanding Mass Spectrometry–Based Shotgun Proteomics Data

Computational proteomics is the data science concerned with the identification and quantification of proteins from high-throughput data and the biological interpretation of their concentration changes, posttranslational modifications, interactions, and subcellular localizations. Today, these data most often originate from mass spectrometry–based shotgun proteomics experiments. In this review, we survey computational methods for the analysis of such proteomics data, focusing on the explanation of the key concepts. Starting with mass spectrometric feature detection, we then cover methods for the identification of peptides. Subsequently, protein inference and the control of false discovery rates are highly important topics covered. We then discuss methods for the quantification of peptides and proteins. A section on downstream data analysis covers exploratory statistics, network analysis, machine learning, and multiomics data integration. Finally, we discuss current developments and provide an outlook on what the near future of computational proteomics might bear.

Netpredictor: R and Shiny package to perform drug-target network analysis and prediction of missing links

BACKGROUND

Netpredictor is an R package for prediction of missing links in any given unipartite or bipartite network. The package provides utilities to compute missing links in a bipartite and well as unipartite networks using Random Walk with Restart and Network inference algorithm and a combination of both. The package also allows computation of Bipartite network properties, visualization of communities for two different sets of nodes, and calculation of significant interactions between two sets of nodes using permutation based testing. The application can also be used to search for top-K shortest paths between interactome and use enrichment analysis for disease, pathway and ontology. The R standalone package (including detailed introductory vignettes) and associated R Shiny web application is available under the GPL-2 Open Source license and is freely available to download.

RESULTS

We compared different algorithms performance in different small datasets and found random walk supersedes rest of the algorithms. The package is developed to perform network based prediction of unipartite and bipartite networks and use the results to understand the functionality of proteins in an interactome using enrichment analysis.

CONCLUSION

The rapid application development envrionment like shiny, helps non programmers to develop fast rich visualization apps and we beleieve it would continue to grow in future with further enhancements. We plan to update our algorithms in the package in near future and help scientist to analyse data in a much streamlined fashion.

Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes

Probing the dynamic control features of biological networks represents a new frontier in capturing the dysregulated pathways in complex diseases. Here, using patient samples obtained from a pancreatic islet transplantation program, we constructed a tissue-specific gene regulatory network and used the control centrality (Cc) concept to identify the high control centrality (HiCc) pathways, which might serve as key pathobiological pathways for Type 2 Diabetes (T2D). We found that HiCc pathway genes were significantly enriched with modest GWAS p-values in the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) study. We identified variants regulating gene expression (expression quantitative loci, eQTL) of HiCc pathway genes in islet samples. These eQTL genes showed higher levels of differential expression compared to non-eQTL genes in low, medium, and high glucose concentrations in rat islets. Among genes with highly significant eQTL evidence, NFATC4 belonged to four HiCc pathways. We asked if the expressions of T2D-associated candidate genes from GWAS and literature are regulated by Nfatc4 in rat islets. Extensive in vitro silencing of Nfatc4 in rat islet cells displayed reduced expression of 16, and increased expression of four putative downstream T2D genes. Overall, our approach uncovers the mechanistic connection of NFATC4 with downstream targets including a previously unknown one, TCF7L2, and establishes the HiCc pathways' relationship to T2D.

Network inference reveals novel connections in pathways regulating growth and defense in the yeast salt response

Cells respond to stressful conditions by coordinating a complex, multi-faceted response that spans many levels of physiology. Much of the response is coordinated by changes in protein phosphorylation. Although the regulators of transcriptome changes during stress are well characterized in Saccharomyces cerevisiae, the upstream regulatory network controlling protein phosphorylation is less well dissected. Here, we developed a computational approach to infer the signaling network that regulates phosphorylation changes in response to salt stress. We developed an approach to link predicted regulators to groups of likely co-regulated phospho-peptides responding to stress, thereby creating new edges in a background protein interaction network. We then use integer linear programming (ILP) to integrate wild type and mutant phospho-proteomic data and predict the network controlling stress-activated phospho-proteomic changes. The network we inferred predicted new regulatory connections between stress-activated and growth-regulating pathways and suggested mechanisms coordinating metabolism, cell-cycle progression, and growth during stress. We confirmed several network predictions with co-immunoprecipitations coupled with mass-spectrometry protein identification and mutant phospho-proteomic analysis. Results show that the cAMP-phosphodiesterase Pde2 physically interacts with many stress-regulated transcription factors targeted by PKA, and that reduced phosphorylation of those factors during stress requires the Rck2 kinase that we show physically interacts with Pde2. Together, our work shows how a high-quality computational network model can facilitate discovery of new pathway interactions during osmotic stress.

Cells sense and respond to stressful environments by utilizing complex signaling networks that integrate diverse signals to coordinate a multi-faceted physiological response. Much of this response is controlled by post-translational protein phosphorylation. Although many regulators that mediate changes in protein phosphorylation are known, how these regulators inter-connect in a single regulatory network that can transmit cellular signals is not known. It is also unclear how regulators that promote growth and regulators that activate the stress response interconnect to reorganize resource allocation during stress. Here, we developed an integrated experimental and computational workflow to infer the signaling network that regulates phosphorylation changes during osmotic stress in the budding yeast Saccharomyces cerevisiae. The workflow integrates data measuring protein phosphorylation changes in response to osmotic stress with known physical interactions between yeast proteins from large-scale datasets, along with other information about how regulators recognize their targets. The resulting network suggested new signaling connections between regulators and pathways, including those involved in regulating growth and defense, and predicted new regulators involved in stress defense. Our work highlights the power of using network inference to deliver new insight on how cells coordinate a diverse adaptive strategy to stress.

ANAT 2.0: reconstructing functional protein subnetworks

BACKGROUND

ANAT is a graphical, Cytoscape-based tool for the inference of protein networks that underlie a process of interest. The ANAT tool allows the user to perform network reconstruction under several scenarios in a number of organisms including yeast and human.

RESULTS

Here we report on a new version of the tool, ANAT 2.0, which introduces substantial code and database updates as well as several new network reconstruction algorithms that greatly extend the applicability of the tool to biological data sets.

CONCLUSIONS

ANAT 2.0 is an up-to-date network reconstruction tool that addresses several reconstruction challenges across multiple species.

The PathLinker app: Connect the dots in protein interaction networks

PathLinker is a graph-theoretic algorithm for reconstructing the interactions in a signaling pathway of interest. It efficiently computes multiple short paths within a background protein interaction network from the receptors to transcription factors (TFs) in a pathway. We originally developed PathLinker to complement manual curation of signaling pathways, which is slow and painstaking. The method can be used in general to connect any set of sources to any set of targets in an interaction network. The app presented here makes the PathLinker functionality available to Cytoscape users. We present an example where we used PathLinker to compute and analyze the network of interactions connecting proteins that are perturbed by the drug lovastatin.

In silico prediction of specific pathways that regulate mesangial cell proliferation in IgA nephropathy

IgA nephropathy is one of the most common forms of primary glomerulonephritis worldwide leading to end-stage renal disease. Proliferation of mesangial cells, i.e., the multifunctional cells located in the intracapillary region of glomeruli, after IgA- dominant immune deposition is the major histologic feature in IgA nephropathy. In spite of several studies on molecular basis of proliferation in these cells, specific pathways responsible for regulation of proliferation are still to be discovered. In this study, we predicted a specific signaling pathway started from transferrin receptor (TFRC), a specific IgA1 receptor on mesangial cells, toward a set of proliferation-related proteins. The final constructed subnetwork was presented after filtration and evaluation. The results suggest that estrogen receptor (ESR1) as a hub protein in the significant subnetwork has an important role in the mesangial cell proliferation and is a potential target for IgA nephropathy therapy. In conclusion, this study suggests a novel hypothesis for the mechanism of pathogenesis in IgA nephropathy and is a reasonable start point for the future experimental studies on mesangial proliferation process in this disease.

A network-based analysis of colon cancer splicing changes reveals a tumorigenesis-favoring regulatory pathway emanating from ELK1

Splicing aberrations are prominent drivers of cancer, yet the regulatory pathways controlling them are mostly unknown. Here we develop a method that integrates physical interaction, gene expression, and alternative splicing data to construct the largest map of transcriptomic and proteomic interactions leading to cancerous splicing aberrations defined to date, and identify driver pathways therein. We apply our method to colon adenocarcinoma and non-small-cell lung carcinoma. By focusing on colon cancer, we reveal a novel tumor-favoring regulatory pathway involving the induction of the transcription factor MYC by the transcription factor ELK1, as well as the subsequent induction of the alternative splicing factor PTBP1 by both. We show that PTBP1 promotes specific RAC1,NUMB, and PKM splicing isoforms that are major triggers of colon tumorigenesis. By testing the pathway's activity in patient tumor samples, we find ELK1,MYC, and PTBP1 to be overexpressed in conjunction with oncogenic KRAS mutations, and show that these mutations increase ELK1 levels via the RAS-MAPK pathway. We thus illuminate, for the first time, a full regulatory pathway connecting prevalent cancerous mutations to functional tumor-inducing splicing aberrations. Our results demonstrate our method is applicable to different cancers to reveal regulatory pathways promoting splicing aberrations.

Linc-RoR promotes c-Myc expression through hnRNP I and AUF1

Linc-RoR was originally identified to be a regulator for induced pluripotent stem cells in humans and it has also been implicated in tumorigenesis. However, the underlying mechanism of Linc-RoR-mediated gene expression in cancer is poorly understood. The present study demonstrates that Linc-RoR plays an oncogenic role in part through regulation of c-Myc expression. Linc-RoR knockout (KO) suppresses cell proliferation and tumor growth. In particular, Linc-RoR KO causes a significant decrease in c-Myc whereas re-expression of Linc-RoR in the KO cells restores the level of c-Myc. Mechanistically, Linc-RoR interacts with heterogeneous nuclear ribonucleoprotein (hnRNP) I and AU-rich element RNA-binding protein 1 (AUF1), respectively, with an opposite consequence to their interaction with c-Myc mRNA. While Linc-RoR is required for hnRNP I to bind to c-Myc mRNA, interaction of Linc-RoR with AUF1 inhibits AUF1 to bind to c-Myc mRNA. As a result, Linc-RoR may contribute to the increased stability of c-Myc mRNA. Although hnRNP I and AUF1 can interact with many RNA species and regulate their functions, with involvement of Linc-RoR they would be able to selectively regulate mRNA stability of specific genes such as c-Myc. Together, these results support a role for Linc-RoR in c-Myc expression in part by specifically enhancing its mRNA stability, leading to cell proliferation and tumorigenesis.

Proteomic Analysis of Dynein-Interacting Proteins in Amyotrophic Lateral Sclerosis Synaptosomes Reveals Alterations in the RNA-Binding Protein Staufen1

Synapse disruption takes place in many neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). However, the mechanistic understanding of this process is still limited. We set out to study a possible role for dynein in synapse integrity. Cytoplasmic dynein is a multisubunit intracellular molecule responsible for diverse cellular functions, including long-distance transport of vesicles, organelles, and signaling factors toward the cell center. A less well-characterized role dynein may play is the spatial clustering and anchoring of various factors including mRNAs in distinct cellular domains such as the neuronal synapse. Here, in order to gain insight into dynein functions in synapse integrity and disruption, we performed a screen for novel dynein interactors at the synapse. Dynein immunoprecipitation from synaptic fractions of the ALS model mSOD1(G93A) and wild-type controls, followed by mass spectrometry analysis on synaptic fractions of the ALS model mSOD1(G93A) and wild-type controls, was performed. Using advanced network analysis, we identified Staufen1, an RNA-binding protein required for the transport and localization of neuronal RNAs, as a major mediator of dynein interactions via its interaction with protein phosphatase 1-beta (PP1B). Both in vitro and in vivo validation assays demonstrate the interactions of Staufen1 and PP1B with dynein, and their colocalization with synaptic markers was altered as a result of two separate ALS-linked mutations: mSOD1(G93A) and TDP43(A315T). Taken together, we suggest a model in which dynein's interaction with Staufen1 regulates mRNA localization along the axon and the synapses, and alterations in this process may correlate with synapse disruption and ALS toxicity.

Systematic identification and correction of annotation errors in the genetic interaction map of Saccharomyces cerevisiae

The yeast mutant collections are a fundamental tool in deciphering genomic organization and function. Over the last decade, they have been used for the systematic exploration of ∼6 000 000 double gene mutants, identifying and cataloging genetic interactions among them. Here we studied the extent to which these data are prone to neighboring gene effects (NGEs), a phenomenon by which the deletion of a gene affects the expression of adjacent genes along the genome. Analyzing ∼90,000 negative genetic interactions observed to date, we found that more than 10% of them are incorrectly annotated due to NGEs. We developed a novel algorithm, GINGER, to identify and correct erroneous interaction annotations. We validated the algorithm using a comparative analysis of interactions from Schizosaccharomyces pombe. We further showed that our predictions are significantly more concordant with diverse biological data compared to their mis-annotated counterparts. Our work uncovered about 9500 new genetic interactions in yeast.

Defining the functional states of Th17 cells

The molecular mechanisms governing T helper (Th) cell differentiation and function have revealed a complex network of transcriptional and protein regulators. Cytokines not only initiate the differentiation of CD4 Th cells into subsets but also influence the identity, plasticity and effector function of a T cell. Of the subsets, Th17 cells, named for producing interleukin 17 (IL-17) as their signature cytokine, secrete a cohort of other cytokines, including IL-22, IL-21, IL-10, IL-9, IFNγ, and GM-CSF.  In recent years, Th17 cells have emerged as key players in host defense against both extracellular pathogens and fungal infections, but they have also been implicated as one of the main drivers in the pathogenesis of autoimmunity, likely mediated in part by the cytokines that they produce. Advances in high throughput genomic sequencing have revealed unexpected heterogeneity in Th17 cells and, as a consequence, may have tremendous impact on our understanding of their functional diversity. The assortment in gene expression may also identify different functional states of Th17 cells. This review aims to understand the interplay between the cytokine regulators that drive Th17 cell differentiation and functional states in Th17 cells.

In vivo regulation of gene expression and T helper type 17 differentiation by RORγt inverse agonists

The orphan nuclear receptor, retinoic acid receptor-related orphan nuclear receptor γt (RORγt), is required for the development and pathogenic function of interleukin-17A-secreting CD4(+) T helper type 17 (Th17) cells. Whereas small molecule RORγt antagonists impair Th17 cell development and attenuate autoimmune inflammation in vivo, the broader effects of these inhibitors on RORγt-dependent gene expression in vivo has yet to be characterized. We show that the RORγt inverse agonist TMP778 acts potently and selectively to block mouse Th17 cell differentiation in vitro and to impair Th17 cell development in vivo upon immunization with the myelin antigen MOG35-55 plus complete Freund's adjuvant. Importantly, we show that TMP778 acts in vivo to repress the expression of more than 150 genes, most of which fall outside the canonical Th17 transcriptional signature and are linked to a variety of inflammatory pathologies in humans. Interestingly, more than 30 genes are related with SMAD3, a transcription factor involved in the Th17 cell differentiation. These results reveal novel disease-associated genes regulated by RORγt during inflammation in vivo, and provide an early read on potential disease indications and safety concerns associated with pharmacological targeting of RORγt.

Regulation of Sec16 levels and dynamics links proliferation and secretion

We currently lack a broader mechanistic understanding of the integration of the early secretory pathway with other homeostatic processes such as cell growth. Here, we explore the possibility that Sec16A, a major constituent of endoplasmic reticulum exit sites (ERES), acts as an integrator of growth factor signaling. Surprisingly, we find that Sec16A is a short-lived protein that is regulated by growth factors in a manner dependent on Egr family transcription factors. We hypothesize that Sec16A acts as a central node in a coherent feed-forward loop that detects persistent growth factor stimuli to increase ERES number. Consistent with this notion, Sec16A is also regulated by short-term growth factor treatment that leads to increased turnover of Sec16A at ERES. Finally, we demonstrate that Sec16A depletion reduces proliferation, whereas its overexpression increases proliferation. Together with our finding that growth factors regulate Sec16A levels and its dynamics on ERES, we propose that this protein acts as an integrator linking growth factor signaling and secretion. This provides a mechanistic basis for the previously proposed link between secretion and proliferation.

The effects of telomere shortening on cancer cells: a network model of proteomic and microRNA analysis

Previously, we have shown that shortening of telomeres by telomerase inhibition sensitized cancer cells to cisplatinum, slowed their migration, increased DNA damage and impaired DNA repair. The mechanism behind these effects is not fully characterized. Its clarification could facilitate novel therapeutics development and may obviate the time consuming process of telomere shortening achieved by telomerase inhibition. Here we aimed to decipher the microRNA and proteomic profiling of cancer cells with shortened telomeres and identify the key mediators in telomere shortening-induced damage to those cells. Of 870 identified proteins, 98 were differentially expressed in shortened-telomere cells. 47 microRNAs were differentially expressed in these cells; some are implicated in growth arrest or act as oncogene repressors. The obtained data was used for a network construction, which provided us with nodal candidates that may mediate the shortened-telomere dependent features. These proteins' expression was experimentally validated, supporting their potential central role in this system.

Signaling hypergraphs

Signaling pathways function as the information-passing mechanisms of cells. A number of databases with extensive manual curation represent the current knowledge base for signaling pathways. These databases motivate the development of computational approaches for prediction and analysis. Such methods require an accurate and computable representation of signaling pathways. Pathways are often described as sets of proteins or as pairwise interactions between proteins. However, many signaling mechanisms cannot be described using these representations. In this opinion, we highlight a representation of signaling pathways that is underutilized: the hypergraph. We demonstrate the usefulness of hypergraphs in this context and discuss challenges and opportunities for the scientific community.

Elucidating influenza inhibition pathways via network reconstruction

Viruses evade detection by the host immune system through the suppression of antiviral pathways. These pathways are thus obscured when measuring the host response to viral infection and cannot be inferred by current network reconstruction methodology. Here we aim to close this gap by providing a novel computational framework for the inference of such inhibited pathways as well as the proteins targeted by the virus to achieve this inhibition. We demonstrate the power of our method by testing it on the response to influenza infection in humans, with and without the viral inhibitory protein NS1, revealing its direct targets and their inhibitory effects.

A minimum-labeling approach for reconstructing protein networks across multiple conditions

Background

The sheer amounts of biological data that are generated in recent years have driven the development of network analysis tools to facilitate the interpretation and representation of these data. A fundamental challenge in this domain is the reconstruction of a protein-protein subnetwork that underlies a process of interest from a genome-wide screen of associated genes. Despite intense work in this area, current algorithmic approaches are largely limited to analyzing a single screen and are, thus, unable to account for information on condition-specific genes, or reveal the dynamics (over time or condition) of the process in question.

Results

We propose a novel formulation for the problem of network reconstruction from multiple-condition data and devise an efficient integer program solution for it. We apply our algorithm to analyze the response to influenza infection and ER export regulation in humans. By comparing to an extant, single-condition tool we demonstrate the power of our new approach in integrating data from multiple conditions in a compact and coherent manner, capturing the dynamics of the underlying processes.

Integrative approaches for finding modular structure in biological networks

A central goal of systems biology is to elucidate the structural and functional architecture of the cell. To this end, large and complex networks of molecular interactions are being rapidly generated for humans and model organisms. A recent focus of bioinformatics research has been to integrate these networks with each other and with diverse molecular profiles to identify sets of molecules and interactions that participate in a common biological function - that is, 'modules'. Here, we classify such integrative approaches into four broad categories, describe their bioinformatic principles and review their applications.

Alternative splicing regulates biogenesis of miRNAs located across exon-intron junctions

The initial step in microRNA (miRNA) biogenesis requires processing of the precursor miRNA (pre-miRNA) from a longer primary transcript. Many pre-miRNAs originate from introns, and both a mature miRNA and a spliced RNA can be generated from the same transcription unit. We have identified a mechanism in which RNA splicing negatively regulates the processing of pre-miRNAs that overlap exon-intron junctions. Computational analysis identified dozens of such pre-miRNAs, and experimental validation demonstrated competitive interaction between the Microprocessor complex and the splicing machinery. Tissue-specific alternative splicing regulates maturation of one such miRNA, miR-412, resulting in effects on its targets that code a protein network involved in neuronal cell death processes. This mode of regulation specifically controls maturation of splice-site-overlapping pre-miRNAs but not pre-miRNAs located completely within introns or exons of the same transcript. Our data present a biological role of alternative splicing in regulation of miRNA biogenesis.

Induction of pathogenic TH17 cells by inducible salt-sensing kinase SGK1

Th17 cells are highly proinflammatory cells critical for clearing extracellular pathogens and for induction of multiple autoimmune diseases¹. IL-23 plays a critical role in stabilizing and reinforcing the Th17 phenotype by increasing expression of IL-23 receptor (IL-23R) and endowing Th17 cells with pathogenic effector functions^{2, 3}. However, the precise molecular mechanism by which IL-23 sustains the Th17 response and induces pathogenic effector functions has not been elucidated. Here, we used transcriptional profiling of developing Th17 cells to construct a model of their signaling network and nominate major nodes that regulate Th17 development. We identified serum glucocorticoid kinase-1 (SGK1), a serine-threonine kinase⁴, as an essential node downstream of IL-23 signaling. SGK1 is critical for regulating IL-23R expression and stabilizing the Th17 cell phenotype by deactivation of Foxo1, a direct repressor of IL-23R expression. SGK1 has been shown to govern Na⁺ transport and salt (NaCl) homeostasis in other cells^{5, 6, 7, 8}. We here show that a modest increase in salt concentration induces SGK1 expression, promotes IL-23R expression and enhances Th17 cell differentiation in vitro and in vivo, accelerating the development of autoimmunity. Loss of SGK1 abrogated Na⁺-mediated Th17 differentiation in an IL-23-dependent manner. These data demonstrate that SGK1 plays a critical role in the induction of pathogenic Th17 cells and provides a molecular insight into a mechanism by which an environmental factor such as a high salt diet triggers Th17 development and promotes tissue inflammation.

iPoint: an integer programming based algorithm for inferring protein subnetworks

Large scale screening experiments have become the workhorse of molecular biology, producing data at an ever increasing scale. The interpretation of such data, particularly in the context of a protein interaction network, has the potential to shed light on the molecular pathways underlying the phenotype or the process in question. A host of approaches have been developed in recent years to tackle this reconstruction challenge. These approaches aim to infer a compact subnetwork that connects the genes revealed by the screen while optimizing local (individual path lengths) or global (likelihood) aspects of the subnetwork. Yosef et al. [Mol. Syst. Biol., 2009, 5, 248] were the first to provide a joint optimization of both criteria, albeit approximate in nature. Here we devise an integer linear programming formulation for the joint optimization problem, allowing us to solve it to optimality in minutes on current networks. We apply our algorithm, iPoint, to various data sets in yeast and human and evaluate its performance against state-of-the-art algorithms. We show that iPoint attains very compact and accurate solutions that outperform previous network inference algorithms with respect to their local and global attributes, their consistency across multiple experiments targeting the same pathway, and their agreement with current biological knowledge.

Adding protein context to the human protein-protein interaction network to reveal meaningful interactions

Interactions of proteins regulate signaling, catalysis, gene expression and many other cellular functions. Therefore, characterizing the entire human interactome is a key effort in current proteomics research. This challenge is complicated by the dynamic nature of protein-protein interactions (PPIs), which are conditional on the cellular context: both interacting proteins must be expressed in the same cell and localized in the same organelle to meet. Additionally, interactions underlie a delicate control of signaling pathways, e.g. by post-translational modifications of the protein partners - hence, many diseases are caused by the perturbation of these mechanisms. Despite the high degree of cell-state specificity of PPIs, many interactions are measured under artificial conditions (e.g. yeast cells are transfected with human genes in yeast two-hybrid assays) or even if detected in a physiological context, this information is missing from the common PPI databases. To overcome these problems, we developed a method that assigns context information to PPIs inferred from various attributes of the interacting proteins: gene expression, functional and disease annotations, and inferred pathways. We demonstrate that context consistency correlates with the experimental reliability of PPIs, which allows us to generate high-confidence tissue- and function-specific subnetworks. We illustrate how these context-filtered networks are enriched in bona fide pathways and disease proteins to prove the ability of context-filters to highlight meaningful interactions with respect to various biological questions. We use this approach to study the lung-specific pathways used by the influenza virus, pointing to IRAK1, BHLHE40 and TOLLIP as potential regulators of influenza virus pathogenicity, and to study the signalling pathways that play a role in Alzheimer's disease, identifying a pathway involving the altered phosphorylation of the Tau protein. Finally, we provide the annotated human PPI network via a web frontend that allows the construction of context-specific networks in several ways.

Protein-protein-interactions (PPIs) participate in virtually all biological processes. However, the PPI map is not static but the pairs of proteins that interact depends on the type of cell, the subcellular localization and modifications of the participating proteins, among many other factors. Therefore, it is important to understand the specific conditions under which a PPI happens. Unfortunately, experimental methods often do not provide this information or, even worse, measure PPIs under artificial conditions not found in biological systems. We developed a method to infer this missing information from properties of the interacting proteins, such as in which cell types the proteins are found, which functions they fulfill and whether they are known to play a role in disease. We show that PPIs for which we can infer conditions under which they happen have a higher experimental reliability. Also, our inference agrees well with known pathways and disease proteins. Since diseases usually affect specific cell types, we study PPI networks of influenza proteins in lung tissues and of Alzheimer's disease proteins in neural tissues. In both cases, we can highlight interesting interactions potentially playing a role in disease progression.

Met kinetic signature derived from the response to HGF/SF in a cellular model predicts breast cancer patient survival

To determine the signaling pathways leading from Met activation to metastasis and poor prognosis, we measured the kinetic gene alterations in breast cancer cell lines in response to HGF/SF. Using a network inference tool we analyzed the putative protein-protein interaction pathways leading from Met to these genes and studied their specificity to Met and prognostic potential. We identified a Met kinetic signature consisting of 131 genes. The signature correlates with Met activation and with response to anti-Met therapy (p<0.005) in in-vitro models. It also identifies breast cancer patients who are at high risk to develop an aggressive disease in six large published breast cancer patient cohorts (p<0.01, N>1000). Moreover, we have identified novel putative Met pathways, which correlate with Met activity and patient prognosis. This signature may facilitate personalized therapy by identifying patients who will respond to anti-Met therapy. Moreover, this novel approach may be applied for other tyrosine kinases and other malignancies.

A network-oriented perspective on cardiac calcium signaling

The normal contractile, electrical, and energetic function of the heart depends on the synchronization of biological oscillators and signal integrators that make up cellular signaling networks. In this review we interpret experimental data from molecular, cellular, and transgenic models of cardiac signaling behavior in the context of established concepts in cell network architecture and organization. Focusing on the cellular Ca(2+) handling machinery, we describe how the plasticity and adaptability of normal Ca(2+) signaling is dependent on dynamic network configurations that operate across a wide range of functional states. We consider how (mal)adaptive changes in signaling pathways restrict the dynamic range of the network such that it cannot respond appropriately to physiologic stimuli or perturbation. Based on these concepts, a model is proposed in which pathologic abnormalities in cardiac rhythm and contractility (e.g., arrhythmias and heart failure) arise as a consequence of progressive desynchronization and reduction in the dynamic range of the Ca(2+) signaling network. We discuss how a systems-level understanding of the network organization, cellular noise, and chaotic behavior may inform the design of new therapeutic modalities that prevent or reverse the disease-linked unraveling of the Ca(2+) signaling network.

Systematic identification of gene annotation errors in the widely used yeast mutation collections

The baker's yeast mutation collections are extensively used genetic resources that are the basis for many genome-wide screens and new technologies. Anecdotal evidence has previously pointed to the putative existence of a neighboring gene effect (NGE) in these collections. NGE occurs when the phenotype of a strain carrying a particular perturbed gene is due to the lack of proper function of its adjacent gene. Here we performed a large-scale study of NGEs, presenting a network-based algorithm for detecting NGEs and validating software predictions using complementation experiments. We applied our approach to four datasets uncovering a similar magnitude of NGE in each (7-15%). These results have important consequences for systems biology, as the mutation collections are extensively used in almost every aspect of the field, from genetic network analysis to functional gene annotation.